Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)
文献类型：期刊论文
| 作者 | Cai, Zhe1; Ge, Song1
|
| 刊名 | JOURNAL OF SYSTEMATICS AND EVOLUTION
 |
| 出版日期 | 2017 |
| 卷号 | 55期号:4页码:377-384 |
| 关键词 | machine learning morphological character phytolith Poaceae taxon discrimination |
| ISSN号 | 1674-4918 |
| DOI | 10.1080/17538947.2016.1227380 |
| 文献子类 | Article; Proceedings Paper |
| 英文摘要 | Phytoliths, as one of the important sources of microfossils, have been widely used in paleobotany-related studies, especially in the grass family (Poaceae) where abundant phytoliths are found. Despite great efforts, several challenges remain when phytoliths are used in various studies, including the accurate description of phytolith morphology and the effective utilization of phytolith traits in taxon identification or discrimination. In this study, we analyzed over 1000 phytolith samples from 18 taxa representing seven main genera in the tribe Oryzeae (subfamily Ehrhartoideae) and five taxa in the subfamilies Bambusoideae and Pooideae. By focusing on Oryzeae, which has been extensively investigated in terms of taxonomy and phylogeny, we were able to evaluate the discrimination power of phytoliths at lower taxonomic levels in grasses. With the help of morphometric analysis and by introducing several machine learning algorithms, we found that 87.7% of the phytolith samples could be classified correctly at the genus level. In spite of slightly different performances, all four machine learning algorithms significantly increased the resolving power of phytolith evidence in taxon identification and discrimination compared with the traditional phytolith analysis. Therefore, we propose a pipeline of phytolith analyses based on machine learning algorithms, including data collection, morphometric analysis, model building, and taxon discrimination. The methodology and pipeline presented here should be applied to various studies across different groups of plants. This study provides new insights into the utilization of phytoliths in evolutionary and ecology studies involving grasses and plants in general. |
| 学科主题 | Physical Geography ; Remote Sensing |
| 出版地 | HOBOKEN |
| 电子版国际标准刊号 | 1759-6831 |
| WOS关键词 | CLIMATE-CHANGE ; GRASSES ; RICE ; PHYLOGENY ; SHAPE ; CLASSIFICATION ; MORPHOMETRICS ; DIRECTIONS ; RADIATION ; SEQUENCES |
| 语种 | 英语 |
| WOS记录号 | WOS:000395038600005 |
| 出版者 | WILEY |
| 资助机构 | National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [91231201, 30990240] ; CAS/SAFEA International Partnership Program for Creative Research TeamsChinese Academy of Sciences |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/22141]  |
| 专题 | 植物研究所_系统与进化植物学研究中心_系统与进化植物学研究中心_学位论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Systemat & Evolutionary Bot, Beijing 100093, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Cai, Zhe,Ge, Song. Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)[J]. JOURNAL OF SYSTEMATICS AND EVOLUTION,2017,55(4):377-384. |
| APA | Cai, Zhe,&Ge, Song.(2017).Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae).JOURNAL OF SYSTEMATICS AND EVOLUTION,55(4),377-384. |
| MLA | Cai, Zhe,et al."Machine learning algorithms improve the power of phytolith analysis: A case study of the tribe Oryzeae (Poaceae)".JOURNAL OF SYSTEMATICS AND EVOLUTION 55.4(2017):377-384. |
入库方式： OAI收割
来源：植物研究所
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